{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:5IXMTU3A3BY53343LRBJK3ZEXR","short_pith_number":"pith:5IXMTU3A","schema_version":"1.0","canonical_sha256":"ea2ec9d360d871ddef9b5c42956f24bc4bd275217a9feabe0de3a145bdad6bfb","source":{"kind":"arxiv","id":"2110.03435","version":1},"attestation_state":"computed","paper":{"title":"Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"eess.AS","authors_text":"Alireza Morsali, Arya Aftab, Benoit Champagne, Shahrokh Ghaemmaghami","submitted_at":"2021-10-07T13:16:31Z","abstract_excerpt":"Detecting emotions directly from a speech signal plays an important role in effective human-computer interactions. Existing speech emotion recognition models require massive computational and storage resources, making them hard to implement concurrently with other machine-interactive tasks in embedded systems. In this paper, we propose an efficient and lightweight fully convolutional neural network for speech emotion recognition in systems with limited hardware resources. In the proposed FCNN model, various feature maps are extracted via three parallel paths with different filter sizes. This h"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2110.03435","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2021-10-07T13:16:31Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"5312e39082156d2a3f3e7c981f7afb485de97513e8b8aecfb310ce9c8539fae6","abstract_canon_sha256":"09e115e5456f7c59f232f1f94b176237d1eb3d1e7a282f302244b57fad63a940"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:20:42.682906Z","signature_b64":"2AYv7cgRxQK6KaXN1Gnv6+5g5LQenqsuRHLWZzEJWjP7LSaP4XtU54iDhLWIuUgB3z0rvXEmpoQoyxqFvPSjAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea2ec9d360d871ddef9b5c42956f24bc4bd275217a9feabe0de3a145bdad6bfb","last_reissued_at":"2026-07-05T03:20:42.682472Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:20:42.682472Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"eess.AS","authors_text":"Alireza Morsali, Arya Aftab, Benoit Champagne, Shahrokh Ghaemmaghami","submitted_at":"2021-10-07T13:16:31Z","abstract_excerpt":"Detecting emotions directly from a speech signal plays an important role in effective human-computer interactions. Existing speech emotion recognition models require massive computational and storage resources, making them hard to implement concurrently with other machine-interactive tasks in embedded systems. In this paper, we propose an efficient and lightweight fully convolutional neural network for speech emotion recognition in systems with limited hardware resources. In the proposed FCNN model, various feature maps are extracted via three parallel paths with different filter sizes. This h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.03435","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2110.03435/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2110.03435","created_at":"2026-07-05T03:20:42.682529+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.03435v1","created_at":"2026-07-05T03:20:42.682529+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.03435","created_at":"2026-07-05T03:20:42.682529+00:00"},{"alias_kind":"pith_short_12","alias_value":"5IXMTU3A3BY5","created_at":"2026-07-05T03:20:42.682529+00:00"},{"alias_kind":"pith_short_16","alias_value":"5IXMTU3A3BY53343","created_at":"2026-07-05T03:20:42.682529+00:00"},{"alias_kind":"pith_short_8","alias_value":"5IXMTU3A","created_at":"2026-07-05T03:20:42.682529+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.07041","citing_title":"Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5IXMTU3A3BY53343LRBJK3ZEXR","json":"https://pith.science/pith/5IXMTU3A3BY53343LRBJK3ZEXR.json","graph_json":"https://pith.science/api/pith-number/5IXMTU3A3BY53343LRBJK3ZEXR/graph.json","events_json":"https://pith.science/api/pith-number/5IXMTU3A3BY53343LRBJK3ZEXR/events.json","paper":"https://pith.science/paper/5IXMTU3A"},"agent_actions":{"view_html":"https://pith.science/pith/5IXMTU3A3BY53343LRBJK3ZEXR","download_json":"https://pith.science/pith/5IXMTU3A3BY53343LRBJK3ZEXR.json","view_paper":"https://pith.science/paper/5IXMTU3A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.03435&json=true","fetch_graph":"https://pith.science/api/pith-number/5IXMTU3A3BY53343LRBJK3ZEXR/graph.json","fetch_events":"https://pith.science/api/pith-number/5IXMTU3A3BY53343LRBJK3ZEXR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5IXMTU3A3BY53343LRBJK3ZEXR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5IXMTU3A3BY53343LRBJK3ZEXR/action/storage_attestation","attest_author":"https://pith.science/pith/5IXMTU3A3BY53343LRBJK3ZEXR/action/author_attestation","sign_citation":"https://pith.science/pith/5IXMTU3A3BY53343LRBJK3ZEXR/action/citation_signature","submit_replication":"https://pith.science/pith/5IXMTU3A3BY53343LRBJK3ZEXR/action/replication_record"}},"created_at":"2026-07-05T03:20:42.682529+00:00","updated_at":"2026-07-05T03:20:42.682529+00:00"}